1. Field of Invention
The invention relates to associating entities and information with behavior. More particularly, the invention relates to a system and a process for targeting and delivering advertising, coupons, products, or informational content to users based upon observed behavior.
2. Description of the Related Technology
The widespread availability of the World Wide Web (web) and Internet services has resulted in a unique set of advertising opportunities. Unlike conventional "broadcast" media, such as television and radio, the web is a "narrowcast" medium that allows the user to have higher levels of control over the information they receive. Since users can control the retrieval of information, the advertising techniques utilized in the conventional broadcast model has become less effective and can alienate potential customers as a result of the "shotgun" effect. The potential of selectively targeting advertisements on a user-by-user basis has been unrealized due to the difficulty in performing meaningful targeting of customers. The current generation of web ad selection engines utilize a number of partially successful techniques to target customers. Typically, the effectiveness of these techniques is measured in terms of audience response rates. Audience response, also called "clickthrough", is evaluated by counting the number of users that click on a "banner advertisement" contained on a web page which is presented to the user. Clicking on the banner advertisement typically takes the user to the web site of the advertiser where additional information about a product or service is provided. In the current Internet advertising environment, clickthrough is the best measure of the effectiveness of advertising techniques. Consequently, the value of advertising is directly related to the effectiveness of the ad. Therefore, the maximization of clickthrough is of paramount importance for most web sites for both practical and financial reasons.
Some current banner advertising selection techniques are listed in the tables below. These techniques are divided into two classes. The first class of advertising selection techniques, shown in Table 1, use simplistic first generation techniques that are based only on static a priori information. The second class of advertising selection techniques, shown in Table 2, utilizes some of the techniques used in the more sophisticated second generation ad selection systems which may take into account some measure of user behavior, such as a user query, or make use of predisclosed user preferences. However, these more sophisticated objectives often only complicate the problem. In Table 1 and Table 2 certain disadvantages of each method are given, however, the Tables are not meant to be all inclusive so not all disadvantages may be shown.
TABLE 1 ______________________________________ First Generation Ad Targeting Techniques. Method Disadvantage ______________________________________ Domain name of Provides no significant user insight; Does not user browser account for user behavior Implies user behavior is related to usage domain Provides poor clickthrough Browser type Implies user behavior is related to usage browser type/version Provides poor clickthrough System type Implies user behavior is related to system type Provides poor clickthrough Service provider Implies user behavior is related to usage domain Provides poor clickthrough Geography/ Requires up-to-date knowledge base of IP address location versus location Provides poor clickthrough Provides no significant user insight Site SIC Code Requires up-to-date knowledge base of SIC code versus IP address Provides somewhat improved user clickthrough Provides no significant user insight Company size Requires up-to-date knowledge base of SIC code versus IP address Provides no significant user insight Implies user behavior is related to size of company Provides poor clickthrough Knowledge base Requires maintenance of knowledge base techniques Costly to maintain Cannot incorporate observed user behavior ______________________________________
TABLE 2 ______________________________________ Second Generation Ad Targeting Techniques. Method Disadvantage ______________________________________ User registration Requires user willingness to disclose information information Requires many judgments on products for effective operation Requires a substantial preexisting judgment database Search key words Misses ads that are conceptually close but do not contain key word Restricts available inventory Requires careful choice of key terms Hand targeting of Very labor intensive; does not scale well ads to sites or Humans not good at manual targeting for large pages numbers of ads Hand analysis of Very labor intensive; does not scale well site or locations Humans not good at manual analysis of large in a site numbers of sites Random Poor response rates presentation Alienates users Date/time Poor response rates Hand analyzed Very labor intensive sites High cost ______________________________________
In the current web environment, users utilize search services to find relevant or interesting information. These search services provide a potential focus for the identification of user behavior as defined by the searches they execute, the web pages they view and positions in the directory hierarchy they visit. However, existing banner advertising selection techniques fail to analyze this behavior when selecting an advertisement. These search services provide an opportunity for presentation of user specific advertising.
The conventional techniques shown in Table 1 and Table 2, use hand coded associations and lists to perform customer "targeting." Certain techniques may use only the current user query as part of ad targeting. Each of the methods shown in Tables 1 and 2 may be associated with five general categories of advertisement selection techniques. These categories include:
(i) rule-based categorization; PA1 (ii) keyword based ad selection; PA1 (iii) assigning ads to individual web pages or sites; PA1 (iv) assigning ads to branches in hierarchical organizations of pages; and PA1 (v) collaborative filtering.
A description of each category is provided below.
Rule Based Ad Selection
Rule based ad selection uses available information and rules to select an appropriate ad. This technique can be effective if large numbers of rules are manually coded and the rule developer has a deep understanding of the problem domain. However, one problem with this system is that human intellectual effort is required to write or maintain the rules. The development of a rule base may be very expensive and time consuming.
Additionally, the rules for advertisement selection are limited to available variables with discrete values and provide "brittle" decision boundaries. These decision boundaries are typically binary and are frequently mishandled.
Rule based ad selection requires extensive knowledge about the targeted operating domain. Even with computer-aided tools, a knowledge engineer is required to develop the rules and administer the system. Furthermore, humans have demonstrated time and again, that they are poor at encoding rules. This observation is particularly true when large numbers of variables are encompassed within the scope of the problem being modeled.
Keyword Based Ad Selection
In keyword based ad selection systems, the ads are selected on the basis of one or more user provided words. When an observed user behavior (typically a user issued query) contains a known keyword, one of the ads, which is manually associated with the keyword, is selected for display. This technique provides good response rates for the keywords chosen. However, a major drawback with this approach is the system administrator must manually choose the keywords associated with each ad. This technique, based on intellectual effort and deep knowledge of the ad-specific domain, is time consuming and error prone. Additionally, the "inventory" of keywords at a site quickly becomes sold out. Lastly, with keyword based ad selection techniques, the ad selection process does not account for previous user behavior. The ad selector only uses a set of human-selected keywords in the current inventory based upon the current search query. Synonyms of user provided words are not automatically targeted without a thesaurus or synonym list.
Assigning Ads To Individual Pages
Ads are sometimes manually targeted to individual web pages. This method requires human intellectual effort to match an advertisement to a web page. However, such effort is usually prohibitive in large scale sites containing thousands of web pages.
Assigning Ads to Branches with Hierarchies of Pages
Ads are sometimes manually targeted to hierarchies or categorizations of pages called page ontologies. This technique may provide effective performance if the human making the assignment of the ad to a branch of the hierarchy has a good feel for the content of the site and understanding of the intended viewer. However, human intellectual effort is required to select the appropriate branch or category for the ad.
Assigning Ads To Web Sites
Ads are sometimes manually targeted to all of the pages in a web site. However, as was seen with many of the other systems, human intellectual effort is required to select the appropriate ad for the web site. This method is inflexible in that an advertiser cannot automatically target an ad to the best pages within a site. Typically, advertisers want to display different ads for each page in a site. Another problem with assigning ads to web sites is that the ads served to the user are not context dependent in that they fail to utilize user specific information.
Collaborative Filtering Techniques
Collaborative filtering (CF) techniques have been proposed for the problem of selectively targeting specific ads to web users. The CF approach requires large numbers of users to formally register with the system and make preference judgments about the quality of the ads, coupons and information content they receive. Under certain limited circumstances, this technique may provide some degree of effective matching in small scale tests. However, for large tasks, such as found on the Internet, the algorithms become both computationally intractable and impractical. Additionally, the CF techniques perform best when the universe of entities are static rather than time evolving. In the Internet market, static content is the exception, not the norm. Furthermore, the utility of the CF approach is directly related to the numbers of users that have made preference judgments. Typically, users are reticent to spend a substantial amount of effort which is required to make these judgments both because of personal time limitations and concerns about privacy. As such, data acquisition becomes a major problem for the advertiser.
CF solutions typically require a knowledge engineer to implement a system of sparse vectors of a very high dimension, where the dimension of the problem space is equal to the number of entities under consideration. However, this solution has been found to be computationally intractable. Another problem with CF techniques is that the ad selection software is examining for orthogonal relationships which requires a substantial number of statistically complicated steps. The ad selection software must find close neighbors in the vector space and suggesting coordinates not in common between the neighbors.
The existing ad selection systems, particularly in coupon and print media advertising, fail to address the interactive nature of the Internet and electronic commerce. Advertisers need to be able to identify users of specific interests, track those interests over time and disseminate, in a highly selective way, information, advertising, coupons and product offerings that will be of interest to the user. Additionally, advertisers need to track user interests and behavior in a real-time manner.
Therefore, advertisers need a system which is sensitive to user behavior for targeting advertising, coupons, products and information content. This system should enable the targeted marketing in real-time and to a granularity of an individual user as well as groups of users that have similar behavioral characteristics.